Ling T.

Key Skills

Software

No software listed

Top Subject Matter

AI model evaluation

Rlhf Domain Expertise

Text Generation

Top Data Types

Text

Image

Video

Top Task Types

RLHF

Bounding Box

Freelancer Overview

AI/ML Researcher - RLHF & Model Evaluation. Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal and Proprietary Tooling. Education includes Bachelor of Engineering, Southern University of Science and Technology (2026). AI-training focus includes data types such as Text and labeling workflows including RLHF, Evaluation, and Rating.

Labeling Experience

Data Annotator - Multilingual AI Data Annotation

Text

This role included annotation and evaluation of AI-generated bilingual data, leveraging fluency in both Chinese and English. Tasks involved data annotation, quality assessment, and cultural/linguistic validation of outputs. The work supported multilingual AI model training and performance benchmarking. • Annotated and evaluated bilingual datasets for AI models. • Performed data quality checks and ensured linguistic and cultural relevance. • Supported multilingual model training through balanced annotation. • Validated outputs for both accuracy and appropriateness.

2025 - Present

AI/ML Researcher - Model Evaluation & Benchmarking

Text

The role centered on comprehensive model evaluation and benchmarking, with emphasis on A/B testing and qualitative error analysis. Responsibilities included systematic testing and performance review of AI models across diverse scenarios. The work contributed directly to frontier model improvement and robustness assessments. • Conducted model evaluation and benchmarking using quantitative and qualitative tools. • Performed safety, robustness, and real-world scenario testing. • Utilized FID/KID metrics and qualitative error analysis for model assessment. • Supported iterative model improvement through detailed documentation and feedback.

2025 - Present

AI/ML Researcher - RLHF & Model Evaluation

TextRLHF

This experience involved conducting reinforcement learning from human feedback (RLHF) to compare and evaluate AI model outputs. The focus was on preference data collection and fine-grained documentation of qualitative differences. The role required systematic A/B evaluation, error analysis, and rigorous model performance documentation. • Compared multiple model versions using qualitative and quantitative metrics. • Documented nuanced quality differences in outputs for preference data. • Performed rigorous A/B comparisons and contributed to model variants evaluation. • Used t-SNE and FID/KID metrics for assessment and failure analysis.

2025 - Present

Education

Work History